Big Data - What Marketers Need to Know

The fastest climbers on the big data learning curve represent the future of marketing. Are you prepared?

The term "big data" is used to describe datasets so large that they become challenging to work with using traditional data management software. New technologies make managing and analyzing big data easier and more cost effective than ever before, and this shift is changing the very nature of marketing. In fact, IT industry analyst firm Forrester reports that marketing departments now drive more than 45 percent of big data initiatives. Let's consider the implications:

1. Your survival hinges on how quickly you recognize that big data trumps intuition for delivering insight and relevance.

Insight into customers and markets is every marketer's most important strategic resource. When that insight is backed up with data, it amplifies the power marketers wield exponentially. And as marketers gain access to more data, both the quantity and quality of their insights increase.

Relevance is every marketer's most important objective - delivering the right message to the right person at the right time for the right price. The digital devices that now dominate consumer attention - computers, set-top boxes, smartphones, gaming platforms, and more - generate massive volumes of data that turn nearly every marketing channel into a direct response channel. These new direct-response feedback loops make big data analytics the most powerful tool marketers have for delivering relevance.

Here's the bottom line: big data analysis is now central to every marketer's success.

2. You will need to take charge of your firm's marketing technology strategy in order to succeed.

Marketers must control their strategic resources - the data and technology required in order to deliver insight and relevance. Collaborate with corporate IT on their security, compliance, reliability, and efficiency objectives, but don't let IT become an external dependency impacting your path to success. Dotted lines and IT chargebacks are your enemy - marketers must control the budget, people, and technology required for the big data analysis that delivers insight and relevance. This is why IT industry analyst firm Gartner projects that by 2017 the CMO will spend more money on IT than the CIO.

3. There is no one-size-fits-all approach for big data.

Your path to success may not look like those of other organizations. There is no single tool that is equally capable of solving every type of big data problem. Marketing professionals will hear many references to technologies like Hadoop, NoSQL, MPP (massively parallel processing) databases, in-memory databases, streaming/CEP (complex event processing) engines - each does its own thing, so it's important to ask what these technologies can do for your business instead of focusing on the technology itself. When making the choice on technology, don't assume that your data and analytical workload matches that of another firm and use that firm's success as the sole basis for making your selection. Proof-of-concept testing with your firm's own data and analytical workload is critically important to minimize risk and set your company up for success in your big data initiative.

4. Software as a service (SaaS) solutions for marketers are great, but they won't solve all of your big data challenges.

SaaS solutions for marketers hide the underlying complexities of the infrastructure technologies listed above and are a good place for marketers to start. Many SaaS firms are working on the problem, but none yet provide a simple solution that delivers complete cross-channel campaign visibility, audience insights, and actionability. Most marketing tools don't talk to each other. As long as these tools operate on independent data sets, much of the value in the underlying data they create is lost. This is why solving the attribution analysis problem is so hard. It's also why many marketers build their own solutions to this problem using various big data platforms referenced above. These big data technology breakthroughs in price, performance, and simplicity now enable marketing problems to be solved in ways that were previously not possible - unifying the data silos created by disparate marketing technologies to transform all of that data into a strategic marketing asset.

5. Big data is forcing chief marketing officers to grapple with the most significant human resource challenge in the history of marketing.

Forrester reported that 75 percent of CMOs were rearranging their teams last year to keep pace with the demands of digital media. Big data is forcing chief marketing officers to find new talent. Bringing in outside experts to reduce risk is smart, but ultimately, marketers must build an internal competency around their data and the technology that unlocks its value. Attracting the best data scientists requires a big data initiative that truly changes the game for your firm backed up by a public commitment to its success from your entire executive team. If you can't make it happen in your current firm, find one where you can.

ABOUT THE AUTHOR

Brad Terrell is responsible for maximizing the value that Netezza technology delivers to the many innovative digital media firms that power their large scale data analysis initiatives with Netezza's analytics appliances.

Before joining Netezza (IBM acquired Netezza November 2010), Brad led digital media initiatives at Endeca as director of business development and helped fuel the firm's rapid growth from $10 million to more than $100 million in annual revenue. Prior to Endeca, Brad helped design and launch products including the Spyglass Mosaic web browser, IBM PowerPC microprocessor, H&R Block online investing website, and FlightSafety International's FAA Level C-certified flight simulators.

Brad's entrepreneurial experience includes co-founding and serving as president of ElectricWish.com, ranked among the top five e-commerce sites on the web by ZDNet in 1999, and M-Nova, a pioneer in product development outsourcing to Eastern Europe. Brad holds an MBA from MIT Sloan and a computer science degree from Rice University.

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